For much of industrial history, manufacturers won by mastering scale. They built larger plants, longer production runs, standardized processes, and global supply chains designed to lower unit cost and increase consistency. That logic still matters. But it is no longer the only source of advantage.

The next advantage comes from proximity. By proximity, I mean the ability to move value closer to the moment and location at which a need emerges. In manufacturing, that might mean producing closer to the customer, configuring products later, responding faster to demand, or correcting a process before a small variation becomes a larger quality problem.

Machine vision sits at the center of this shift. Too often, vision systems are specified around a narrow question: Can the system detect the defect? That is an essential starting point, but it is no longer enough.

One of my first clients in my early days at McKinsey was a company producing inexpensive image-capture devices that manufacturers could affordably embed throughout the production flow. Even then, the promise was clear: when manufacturers could see more of what was happening inside the operation, they could make better decisions.

Those technologies have advanced dramatically since, and companies are continuing to invest heavily in smarter, more connected operations. In Deloitte’s 2025 survey of 600 manufacturing executives, 92% said smart manufacturing would become a primary driver of competitiveness over the next three years.[1]

Yet the value of a connected factory does not come from generating more data. It comes from turning the right data into better and faster decisions.

A vision application may identify a condition almost immediately, while the organization still takes hours or days to understand its cause and respond. The result is distance: distance between what the system sees and what the operation does.

The opportunity is to close that distance. That does not mean every vision system should automatically adjust equipment or perform complex analytics. In many applications, a focused pass-or-fail inspection remains exactly the right solution.

But engineering and manufacturing teams should consider the broader operational role of the information being captured. What decision will it support? Where should the result go? What response should follow?

5 Questions to Help Teams Connect Machine Vision Data to Better Operational Decisions

1. What decision must the system support? The first question should not be, “Which camera should we use?” It should be, “What decision must this system support?” That distinction changes the way a system is designed.

A system intended to verify whether a component is present has different requirements from one intended to measure gradual process variation. A system that triggers an immediate reject action differs from one that supplies information to maintenance or engineering.

Before selecting hardware or software, define the decision clearly. What condition must the system identify? How quickly must the result be available? Who or what receives it? What are the consequences of a false acceptance, false rejection, or delayed response?

The answers will influence the imaging method, inspection location, cycle-time requirements, data architecture, and validation approach. A technically accurate inspection can still create limited value if its output is not connected to a useful decision.

2. Where does the relevant signal first become visible? Manufacturers often inspect products near the end of production because that is where final quality can be verified. But the source of a problem may have appeared much earlier. An AI and digital twin technologies allow us to predict more accurately problems before they emerge. 

A dimensional variation found at final inspection may have originated when a tool began to wear. An assembly defect may have started with inconsistent placement several stations upstream. The later the issue is discovered, the more labor, material, and production time may already have been invested. This is proximity applied inside the factory. The goal is not only to detect a defect. It is to move closer to the condition that created it.

Teams should therefore ask where the relevant signal first becomes detectable and reliable enough to support a decision. The earliest possible inspection point is not always the best one. The feature may not yet be visible, the part may be inaccessible, or lighting and presentation may make the image unstable.

The objective is to identify the earliest practical point at which dependable information can still improve the outcome. Catching a defective product protects the customer. Recognizing the condition that produces the defect may prevent additional defects. 

3. Is the system detecting an event or revealing a pattern? Many machine vision applications identify individual exceptions. Is the label present? Is the part aligned? Is the dimension within tolerance? These decisions are valuable, but they may not reveal whether the larger process is changing. 

A single rejected item may be random. A pattern of small deviations may indicate equipment wear, process drift, material variation, or another condition that deserves attention before failure rates rise.

To reveal those patterns, the system may need to preserve more than a binary result. Measurements, classifications, timestamps, images, product configuration, machine settings, material batch, shift, or equipment condition may all provide useful context.

That does not mean the camera or vision controller must perform the analysis itself. Its role may be to generate reliable, structured data that another system, such as a manufacturing execution system, quality platform, historian, or analytics environment, can evaluate over time.

The Association for Advancing Automation (A3) has noted that machine vision inspection data can support quality control, defect trending, and Pareto analysis.[2] Rockwell Automation’s 2025 State of Smart Manufacturing Report also found that 38% of surveyed manufacturers planned to use data from existing sources to improve product-quality monitoring.[3]

The data may already exist. The challenge is ensuring it remains accessible, interpretable, and connected to the decisions it can improve.

4. What response should follow the observation? Once a system detects a condition, what should happen next? Some results may justify an immediate automated response, such as rejecting an item, selecting a predefined robot path, or stopping a process when a validated critical condition appears.

Other results may be better suited to an alert, maintenance request, engineering review, or accumulated trend report. Not every observation should trigger intervention. A single exception may not justify changing a stable process, and an automated adjustment based on incomplete information could introduce more variation.

Closed-loop changes require careful validation. Teams must confirm that the visual measurement is repeatable, that the relationship between the observation and the adjustment is understood, and that safeguards are in place.

The objective is not to automate every response. It is to define the appropriate response and reduce the time required to initiate it. Teams should map the path from observation to action. Where is information delayed? Where does it require manual transfer? Where is responsibility unclear? Which decisions could be accelerated through better integration, alerts, workflows, or access to data?

The vision application may already be fast enough. The larger operating system may not be.

5. What variation should the system tolerate? Traditional manufacturing systems achieve efficiency by minimizing variation. But many production environments now handle more configurations, shorter runs, mixed models, supplier substitutions, and changing customer requirements.

This is one reason proximity matters. As value moves closer to actual demand, manufacturing systems must handle more variation without surrendering consistency.

Machine vision can support this flexibility when it is deliberately designed and validated to recognize the expected range of products, orientations, materials, and operating conditions.

A vision system does not automatically adapt to every unanticipated variation. It can recognize only the conditions its hardware, software, models, and inspection logic have been configured to handle.

Teams should therefore define two boundaries clearly:

• What variation is acceptable and should be tolerated?
• What variation is meaningful and must be flagged?

If the boundaries are too narrow, normal variation may create unnecessary false rejects. If they are too broad, the system may accept conditions that matter. Designing for controlled variation allows manufacturers to gain flexibility without surrendering consistency.

Closing the Distance

Machine vision gives manufacturers the ability to see change as it happens. But seeing is not the same as responding. The strategic value comes when reliable visual information reaches the right decision-maker, human or automated, while it can still improve the outcome.

A system that detects defects and performs an immediate quality-control task is valuable. A system whose results also help the organization identify patterns, understand changing conditions, and choose an appropriate response can create value beyond the inspection station.

That is why machine vision should be understood not only as an inspection technology, but as a proximity technology. It helps manufacturers move closer to the signal, closer to the source of variation, and closer to the moment when action still matters.

The next generation of machine vision systems will not be judged only by how accurately they see; they will also be judged by how effectively they help the manufacturer act.

References

[1] Deloitte, 2025 Smart Manufacturing and Operations Survey, based on 600 manufacturing executives.

[2] Association for Advancing Automation (A3), guidance on using machine vision inspection data for quality control, defect trending, and Pareto analysis.

[3] Rockwell Automation, 2025 State of Smart Manufacturing Report, based on more than 1,500 manufacturing respondents.